Overview

Dataset statistics

Number of variables26
Number of observations1123
Missing cells1970
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory220.6 KiB
Average record size in memory201.1 B

Variable types

Text5
Numeric9
Unsupported7
Categorical2
DateTime2
Boolean1

Alerts

GitHub Repository ID is highly overall correlated with Duration Created to Now in YearsHigh correlation
GitHub Stars is highly overall correlated with GitHub Forks and 4 other fieldsHigh correlation
GitHub Forks is highly overall correlated with GitHub Stars and 4 other fieldsHigh correlation
GitHub Subscribers is highly overall correlated with GitHub Stars and 4 other fieldsHigh correlation
GitHub Open Issues is highly overall correlated with GitHub Stars and 4 other fieldsHigh correlation
GitHub Contributors is highly overall correlated with GitHub Stars and 4 other fieldsHigh correlation
GitHub Network Count is highly overall correlated with GitHub Stars and 4 other fieldsHigh correlation
Duration Created to Now in Years is highly overall correlated with GitHub Repository IDHigh correlation
GitHub Repo Archived is highly imbalanced (90.9%)Imbalance
category is highly imbalanced (75.0%)Imbalance
Project Homepage has 539 (48.0%) missing valuesMissing
GitHub License Type has 551 (49.1%) missing valuesMissing
GitHub Description has 54 (4.8%) missing valuesMissing
GitHub Organization has 826 (73.6%) missing valuesMissing
GitHub Stars is highly skewed (γ1 = 21.55490466)Skewed
GitHub Forks is highly skewed (γ1 = 25.3051631)Skewed
GitHub Subscribers is highly skewed (γ1 = 22.78021783)Skewed
GitHub Network Count is highly skewed (γ1 = 25.30491864)Skewed
GitHub Repository ID has unique valuesUnique
Project Repo URL has unique valuesUnique
Date Created has unique valuesUnique
Date Most Recent Commit has unique valuesUnique
Project Landscape Category is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Topics is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Detected Languages is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Most Recent Commit is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Negative Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Stars has 44 (3.9%) zerosZeros
GitHub Forks has 435 (38.7%) zerosZeros
GitHub Subscribers has 52 (4.6%) zerosZeros
GitHub Open Issues has 674 (60.0%) zerosZeros
GitHub Contributors has 27 (2.4%) zerosZeros
GitHub Network Count has 435 (38.7%) zerosZeros

Reproduction

Analysis started2023-10-16 17:47:39.523045
Analysis finished2023-10-16 17:47:47.149763
Duration7.63 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Distinct1090
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:47.351903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length100
Median length65
Mean length15.050757
Min length3

Characters and Unicode

Total characters16902
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1065 ?
Unique (%)94.8%

Sample

1st rowpandas
2nd rownumpy
3rd rowarrow
4th rowduckdb
5th rowparquet-mr
ValueCountFrequency (%)
single-cell-analysis 8
 
0.7%
single-cell-rna-seq-analysis 7
 
0.6%
single_cell_analysis 7
 
0.6%
singlecellanalysis 4
 
0.4%
single-cell-rna-seq 4
 
0.4%
singlecell 3
 
0.3%
orchestratingsinglecellanalysis 3
 
0.3%
single-cell-rna-sequencing-analysis 2
 
0.2%
cytominer 2
 
0.2%
spectre 2
 
0.2%
Other values (1060) 1081
96.3%
2023-10-16T11:47:47.676236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1443
 
8.5%
l 1339
 
7.9%
s 1259
 
7.4%
i 1025
 
6.1%
a 1012
 
6.0%
n 949
 
5.6%
- 847
 
5.0%
c 778
 
4.6%
o 695
 
4.1%
t 617
 
3.7%
Other values (55) 6938
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12372
73.2%
Uppercase Letter 2819
 
16.7%
Dash Punctuation 847
 
5.0%
Connector Punctuation 431
 
2.5%
Decimal Number 413
 
2.4%
Other Punctuation 20
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1443
11.7%
l 1339
10.8%
s 1259
10.2%
i 1025
 
8.3%
a 1012
 
8.2%
n 949
 
7.7%
c 778
 
6.3%
o 695
 
5.6%
t 617
 
5.0%
r 573
 
4.6%
Other values (16) 2682
21.7%
Uppercase Letter
ValueCountFrequency (%)
A 427
15.1%
C 394
14.0%
S 374
13.3%
R 231
 
8.2%
N 205
 
7.3%
M 132
 
4.7%
T 129
 
4.6%
P 128
 
4.5%
I 113
 
4.0%
D 105
 
3.7%
Other values (16) 581
20.6%
Decimal Number
ValueCountFrequency (%)
2 156
37.8%
0 100
24.2%
1 72
17.4%
3 22
 
5.3%
9 21
 
5.1%
8 14
 
3.4%
4 11
 
2.7%
7 8
 
1.9%
6 5
 
1.2%
5 4
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 847
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 431
100.0%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15191
89.9%
Common 1711
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1443
 
9.5%
l 1339
 
8.8%
s 1259
 
8.3%
i 1025
 
6.7%
a 1012
 
6.7%
n 949
 
6.2%
c 778
 
5.1%
o 695
 
4.6%
t 617
 
4.1%
r 573
 
3.8%
Other values (42) 5501
36.2%
Common
ValueCountFrequency (%)
- 847
49.5%
_ 431
25.2%
2 156
 
9.1%
0 100
 
5.8%
1 72
 
4.2%
3 22
 
1.3%
9 21
 
1.2%
. 20
 
1.2%
8 14
 
0.8%
4 11
 
0.6%
Other values (3) 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1443
 
8.5%
l 1339
 
7.9%
s 1259
 
7.4%
i 1025
 
6.1%
a 1012
 
6.0%
n 949
 
5.6%
- 847
 
5.0%
c 778
 
4.6%
o 695
 
4.1%
t 617
 
3.7%
Other values (55) 6938
41.0%

GitHub Repository ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7923865 × 108
Minimum858127
Maximum7.0306429 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:47.772054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum858127
5-th percentile48407428
Q11.4014133 × 108
median2.5217638 × 108
Q33.8810811 × 108
95-th percentile6.0591363 × 108
Maximum7.0306429 × 108
Range7.0220617 × 108
Interquartile range (IQR)2.4796679 × 108

Descriptive statistics

Standard deviation1.7117573 × 108
Coefficient of variation (CV)0.61300875
Kurtosis-0.58841698
Mean2.7923865 × 108
Median Absolute Deviation (MAD)1.2213142 × 108
Skewness0.54510555
Sum3.13585 × 1011
Variance2.9301131 × 1016
MonotonicityNot monotonic
2023-10-16T11:47:47.848299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
858127 1
 
0.1%
292773252 1
 
0.1%
255056675 1
 
0.1%
146028869 1
 
0.1%
232201771 1
 
0.1%
167464808 1
 
0.1%
156759735 1
 
0.1%
255893503 1
 
0.1%
339741407 1
 
0.1%
188461334 1
 
0.1%
Other values (1113) 1113
99.1%
ValueCountFrequency (%)
858127 1
0.1%
908607 1
0.1%
925122 1
0.1%
1571820 1
0.1%
2136580 1
0.1%
2290781 1
0.1%
2425273 1
0.1%
4890816 1
0.1%
5771522 1
0.1%
8678018 1
0.1%
ValueCountFrequency (%)
703064293 1
0.1%
690579583 1
0.1%
689948460 1
0.1%
682462552 1
0.1%
681597755 1
0.1%
679608830 1
0.1%
678475435 1
0.1%
678200716 1
0.1%
677900044 1
0.1%
677883930 1
0.1%

Project Homepage
Text

MISSING 

Distinct206
Distinct (%)35.3%
Missing539
Missing (%)48.0%
Memory size8.9 KiB
2023-10-16T11:47:48.041339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length116
Median length0
Mean length13.777397
Min length0

Characters and Unicode

Total characters8046
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)34.9%

Sample

1st rowhttps://pandas.pydata.org
2nd rowhttps://numpy.org
3rd rowhttps://arrow.apache.org/
4th rowhttp://www.duckdb.org
5th row
ValueCountFrequency (%)
http://nasqar.abudhabi.nyu.edu 2
 
1.0%
https://combine-lab.github.io/salmon 1
 
0.5%
https://www.scrna-tools.org 1
 
0.5%
https://arrow.apache.org 1
 
0.5%
http://www.duckdb.org 1
 
0.5%
https://snakemake.readthedocs.io 1
 
0.5%
http://www.satijalab.org/seurat 1
 
0.5%
https://napari.org 1
 
0.5%
https://scanpy.readthedocs.io 1
 
0.5%
http://scvi-tools.org 1
 
0.5%
Other values (195) 195
94.7%
2023-10-16T11:47:48.339277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 757
 
9.4%
/ 727
 
9.0%
s 489
 
6.1%
i 475
 
5.9%
o 461
 
5.7%
e 413
 
5.1%
. 408
 
5.1%
h 406
 
5.0%
a 366
 
4.5%
p 308
 
3.8%
Other values (55) 3236
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6072
75.5%
Other Punctuation 1339
 
16.6%
Decimal Number 291
 
3.6%
Uppercase Letter 201
 
2.5%
Dash Punctuation 124
 
1.5%
Connector Punctuation 17
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 757
12.5%
s 489
 
8.1%
i 475
 
7.8%
o 461
 
7.6%
e 413
 
6.8%
h 406
 
6.7%
a 366
 
6.0%
p 308
 
5.1%
c 293
 
4.8%
r 283
 
4.7%
Other values (16) 1821
30.0%
Uppercase Letter
ValueCountFrequency (%)
C 29
14.4%
S 27
13.4%
A 23
11.4%
R 16
 
8.0%
M 11
 
5.5%
T 10
 
5.0%
I 9
 
4.5%
N 9
 
4.5%
D 9
 
4.5%
O 9
 
4.5%
Other values (12) 49
24.4%
Decimal Number
ValueCountFrequency (%)
1 63
21.6%
0 58
19.9%
2 42
14.4%
3 23
 
7.9%
4 22
 
7.6%
6 19
 
6.5%
8 17
 
5.8%
7 16
 
5.5%
9 16
 
5.5%
5 15
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 727
54.3%
. 408
30.5%
: 204
 
15.2%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6273
78.0%
Common 1773
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 757
 
12.1%
s 489
 
7.8%
i 475
 
7.6%
o 461
 
7.3%
e 413
 
6.6%
h 406
 
6.5%
a 366
 
5.8%
p 308
 
4.9%
c 293
 
4.7%
r 283
 
4.5%
Other values (38) 2022
32.2%
Common
ValueCountFrequency (%)
/ 727
41.0%
. 408
23.0%
: 204
 
11.5%
- 124
 
7.0%
1 63
 
3.6%
0 58
 
3.3%
2 42
 
2.4%
3 23
 
1.3%
4 22
 
1.2%
6 19
 
1.1%
Other values (7) 83
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 757
 
9.4%
/ 727
 
9.0%
s 489
 
6.1%
i 475
 
5.9%
o 461
 
5.7%
e 413
 
5.1%
. 408
 
5.1%
h 406
 
5.0%
a 366
 
4.5%
p 308
 
3.8%
Other values (55) 3236
40.2%

Project Repo URL
Text

UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:48.565858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length157
Median length91
Mean length45.211932
Min length28

Characters and Unicode

Total characters50773
Distinct characters67
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1123 ?
Unique (%)100.0%

Sample

1st rowhttps://github.com/pandas-dev/pandas
2nd rowhttps://github.com/numpy/numpy
3rd rowhttps://github.com/apache/arrow
4th rowhttps://github.com/duckdb/duckdb
5th rowhttps://github.com/apache/parquet-mr
ValueCountFrequency (%)
https://github.com/pandas-dev/pandas 1
 
0.1%
https://github.com/scverse/scanpy 1
 
0.1%
https://github.com/duckdb/duckdb 1
 
0.1%
https://github.com/apache/parquet-mr 1
 
0.1%
https://github.com/snakemake/snakemake 1
 
0.1%
https://github.com/satijalab/seurat 1
 
0.1%
https://github.com/napari/napari 1
 
0.1%
https://github.com/chris-mcginnis-ucsf/doubletfinder 1
 
0.1%
https://github.com/theislab/single-cell-tutorial 1
 
0.1%
https://github.com/sqjin/cellchat 1
 
0.1%
Other values (1113) 1113
99.1%
2023-10-16T11:47:48.883655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4492
 
8.8%
t 4402
 
8.7%
i 3032
 
6.0%
s 2940
 
5.8%
h 2809
 
5.5%
o 2442
 
4.8%
c 2302
 
4.5%
e 2218
 
4.4%
a 2211
 
4.4%
l 1917
 
3.8%
Other values (57) 22008
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37999
74.8%
Other Punctuation 6758
 
13.3%
Uppercase Letter 3741
 
7.4%
Dash Punctuation 1105
 
2.2%
Decimal Number 739
 
1.5%
Connector Punctuation 431
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4402
 
11.6%
i 3032
 
8.0%
s 2940
 
7.7%
h 2809
 
7.4%
o 2442
 
6.4%
c 2302
 
6.1%
e 2218
 
5.8%
a 2211
 
5.8%
l 1917
 
5.0%
g 1817
 
4.8%
Other values (16) 11909
31.3%
Uppercase Letter
ValueCountFrequency (%)
A 476
12.7%
C 466
12.5%
S 451
12.1%
R 255
 
6.8%
N 238
 
6.4%
M 193
 
5.2%
L 173
 
4.6%
T 173
 
4.6%
P 157
 
4.2%
I 157
 
4.2%
Other values (16) 1002
26.8%
Decimal Number
ValueCountFrequency (%)
2 196
26.5%
0 153
20.7%
1 127
17.2%
9 54
 
7.3%
3 47
 
6.4%
8 43
 
5.8%
4 39
 
5.3%
7 31
 
4.2%
5 27
 
3.7%
6 22
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 4492
66.5%
. 1143
 
16.9%
: 1123
 
16.6%
Dash Punctuation
ValueCountFrequency (%)
- 1105
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41740
82.2%
Common 9033
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4402
 
10.5%
i 3032
 
7.3%
s 2940
 
7.0%
h 2809
 
6.7%
o 2442
 
5.9%
c 2302
 
5.5%
e 2218
 
5.3%
a 2211
 
5.3%
l 1917
 
4.6%
g 1817
 
4.4%
Other values (42) 15650
37.5%
Common
ValueCountFrequency (%)
/ 4492
49.7%
. 1143
 
12.7%
: 1123
 
12.4%
- 1105
 
12.2%
_ 431
 
4.8%
2 196
 
2.2%
0 153
 
1.7%
1 127
 
1.4%
9 54
 
0.6%
3 47
 
0.5%
Other values (5) 162
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4492
 
8.8%
t 4402
 
8.7%
i 3032
 
6.0%
s 2940
 
5.8%
h 2809
 
5.5%
o 2442
 
4.8%
c 2302
 
4.5%
e 2218
 
4.4%
a 2211
 
4.4%
l 1917
 
3.8%
Other values (57) 22008
43.3%

Project Landscape Category
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Stars
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct159
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.7569
Minimum0
Maximum40028
Zeros44
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:48.976560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q316
95-th percentile193.7
Maximum40028
Range40028
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1505.1896
Coefficient of variation (CV)12.674544
Kurtosis512.22399
Mean118.7569
Median Absolute Deviation (MAD)2
Skewness21.554905
Sum133364
Variance2265595.8
MonotonicityDecreasing
2023-10-16T11:47:49.099738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 316
28.1%
2 130
 
11.6%
3 88
 
7.8%
4 66
 
5.9%
0 44
 
3.9%
5 39
 
3.5%
6 28
 
2.5%
8 24
 
2.1%
7 20
 
1.8%
9 18
 
1.6%
Other values (149) 350
31.2%
ValueCountFrequency (%)
0 44
 
3.9%
1 316
28.1%
2 130
11.6%
3 88
 
7.8%
4 66
 
5.9%
5 39
 
3.5%
6 28
 
2.5%
7 20
 
1.8%
8 24
 
2.1%
9 18
 
1.6%
ValueCountFrequency (%)
40028 1
0.1%
24740 1
0.1%
12614 1
0.1%
12393 1
0.1%
2179 1
0.1%
1955 1
0.1%
1947 1
0.1%
1912 1
0.1%
1611 1
0.1%
1592 1
0.1%

GitHub Forks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct96
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.960819
Minimum0
Maximum16810
Zeros435
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:49.260693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile52.9
Maximum16810
Range16810
Interquartile range (IQR)6

Descriptive statistics

Standard deviation574.99328
Coefficient of variation (CV)14.758244
Kurtosis691.24904
Mean38.960819
Median Absolute Deviation (MAD)1
Skewness25.305163
Sum43753
Variance330617.27
MonotonicityNot monotonic
2023-10-16T11:47:49.334540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
38.7%
1 167
 
14.9%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
6 30
 
2.7%
5 25
 
2.2%
8 23
 
2.0%
9 19
 
1.7%
7 18
 
1.6%
Other values (86) 194
17.3%
ValueCountFrequency (%)
0 435
38.7%
1 167
 
14.9%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
5 25
 
2.2%
6 30
 
2.7%
7 18
 
1.6%
8 23
 
2.0%
9 19
 
1.7%
ValueCountFrequency (%)
16810 1
0.1%
8646 1
0.1%
3096 1
0.1%
1332 1
0.1%
1157 1
0.1%
851 1
0.1%
536 1
0.1%
474 1
0.1%
473 1
0.1%
422 1
0.1%

GitHub Subscribers
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct45
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2288513
Minimum0
Maximum1121
Zeros52
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:49.406906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile15
Maximum1121
Range1121
Interquartile range (IQR)3

Descriptive statistics

Standard deviation40.027421
Coefficient of variation (CV)6.4261321
Kurtosis583.17272
Mean6.2288513
Median Absolute Deviation (MAD)1
Skewness22.780218
Sum6995
Variance1602.1945
MonotonicityNot monotonic
2023-10-16T11:47:49.475664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 363
32.3%
2 247
22.0%
3 116
 
10.3%
4 74
 
6.6%
0 52
 
4.6%
5 47
 
4.2%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
Other values (35) 110
 
9.8%
ValueCountFrequency (%)
0 52
 
4.6%
1 363
32.3%
2 247
22.0%
3 116
 
10.3%
4 74
 
6.6%
5 47
 
4.2%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
ValueCountFrequency (%)
1121 1
0.1%
595 1
0.1%
351 1
0.1%
157 1
0.1%
95 1
0.1%
86 1
0.1%
78 1
0.1%
54 1
0.1%
51 1
0.1%
49 1
0.1%

GitHub Open Issues
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.515583
Minimum0
Maximum3910
Zeros674
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:49.546472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile31.9
Maximum3910
Range3910
Interquartile range (IQR)2

Descriptive statistics

Standard deviation181.21088
Coefficient of variation (CV)9.7869389
Kurtosis354.17477
Mean18.515583
Median Absolute Deviation (MAD)0
Skewness18.00145
Sum20793
Variance32837.384
MonotonicityNot monotonic
2023-10-16T11:47:49.624252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 674
60.0%
1 125
 
11.1%
2 53
 
4.7%
3 33
 
2.9%
4 26
 
2.3%
6 26
 
2.3%
8 14
 
1.2%
5 14
 
1.2%
7 13
 
1.2%
19 8
 
0.7%
Other values (69) 137
 
12.2%
ValueCountFrequency (%)
0 674
60.0%
1 125
 
11.1%
2 53
 
4.7%
3 33
 
2.9%
4 26
 
2.3%
5 14
 
1.2%
6 26
 
2.3%
7 13
 
1.2%
8 14
 
1.2%
9 7
 
0.6%
ValueCountFrequency (%)
3910 1
0.1%
3661 1
0.1%
2196 1
0.1%
1019 1
0.1%
906 1
0.1%
701 1
0.1%
547 1
0.1%
424 1
0.1%
363 1
0.1%
309 1
0.1%

GitHub Contributors
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6073019
Minimum0
Maximum435
Zeros27
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:49.696353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile10
Maximum435
Range435
Interquartile range (IQR)2

Descriptive statistics

Standard deviation25.443233
Coefficient of variation (CV)5.5223717
Kurtosis190.13087
Mean4.6073019
Median Absolute Deviation (MAD)0
Skewness13.186791
Sum5174
Variance647.35813
MonotonicityNot monotonic
2023-10-16T11:47:49.761303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 659
58.7%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
0 27
 
2.4%
6 15
 
1.3%
7 11
 
1.0%
11 10
 
0.9%
10 10
 
0.9%
Other values (28) 57
 
5.1%
ValueCountFrequency (%)
0 27
 
2.4%
1 659
58.7%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
6 15
 
1.3%
7 11
 
1.0%
8 9
 
0.8%
9 8
 
0.7%
ValueCountFrequency (%)
435 1
0.1%
411 1
0.1%
367 1
0.1%
280 1
0.1%
253 1
0.1%
190 1
0.1%
150 1
0.1%
125 1
0.1%
83 1
0.1%
79 1
0.1%

GitHub License Type
Categorical

MISSING 

Distinct15
Distinct (%)2.6%
Missing551
Missing (%)49.1%
Memory size8.9 KiB
MIT
211 
GPL-3.0
163 
NOASSERTION
73 
BSD-3-Clause
49 
Apache-2.0
29 
Other values (10)
47 

Length

Max length18
Median length12
Mean length6.7412587
Min length3

Characters and Unicode

Total characters3856
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.7%

Sample

1st rowBSD-3-Clause
2nd rowBSD-3-Clause
3rd rowApache-2.0
4th rowMIT
5th rowApache-2.0

Common Values

ValueCountFrequency (%)
MIT 211
 
18.8%
GPL-3.0 163
 
14.5%
NOASSERTION 73
 
6.5%
BSD-3-Clause 49
 
4.4%
Apache-2.0 29
 
2.6%
CC0-1.0 13
 
1.2%
AGPL-3.0 9
 
0.8%
BSD-2-Clause 7
 
0.6%
GPL-2.0 7
 
0.6%
LGPL-3.0 4
 
0.4%
Other values (5) 7
 
0.6%
(Missing) 551
49.1%

Length

2023-10-16T11:47:49.828504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mit 211
36.9%
gpl-3.0 163
28.5%
noassertion 73
 
12.8%
bsd-3-clause 49
 
8.6%
apache-2.0 29
 
5.1%
cc0-1.0 13
 
2.3%
agpl-3.0 9
 
1.6%
bsd-2-clause 7
 
1.2%
gpl-2.0 7
 
1.2%
lgpl-3.0 4
 
0.7%
Other values (5) 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
- 348
 
9.0%
I 284
 
7.4%
T 284
 
7.4%
0 243
 
6.3%
. 230
 
6.0%
3 226
 
5.9%
M 212
 
5.5%
S 203
 
5.3%
L 188
 
4.9%
P 184
 
4.8%
Other values (26) 1454
37.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2299
59.6%
Decimal Number 530
 
13.7%
Lowercase Letter 449
 
11.6%
Dash Punctuation 348
 
9.0%
Other Punctuation 230
 
6.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 284
12.4%
T 284
12.4%
M 212
9.2%
S 203
8.8%
L 188
8.2%
P 184
8.0%
G 183
8.0%
N 146
6.4%
O 146
6.4%
A 112
 
4.9%
Other values (7) 357
15.5%
Lowercase Letter
ValueCountFrequency (%)
e 89
19.8%
a 87
19.4%
l 59
13.1%
s 59
13.1%
u 57
12.7%
c 31
 
6.9%
h 29
 
6.5%
p 29
 
6.5%
i 3
 
0.7%
r 2
 
0.4%
Other values (2) 4
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 243
45.8%
3 226
42.6%
2 45
 
8.5%
1 13
 
2.5%
4 3
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 348
100.0%
Other Punctuation
ValueCountFrequency (%)
. 230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2748
71.3%
Common 1108
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 284
 
10.3%
T 284
 
10.3%
M 212
 
7.7%
S 203
 
7.4%
L 188
 
6.8%
P 184
 
6.7%
G 183
 
6.7%
N 146
 
5.3%
O 146
 
5.3%
A 112
 
4.1%
Other values (19) 806
29.3%
Common
ValueCountFrequency (%)
- 348
31.4%
0 243
21.9%
. 230
20.8%
3 226
20.4%
2 45
 
4.1%
1 13
 
1.2%
4 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 348
 
9.0%
I 284
 
7.4%
T 284
 
7.4%
0 243
 
6.3%
. 230
 
6.0%
3 226
 
5.9%
M 212
 
5.5%
S 203
 
5.3%
L 188
 
4.9%
P 184
 
4.8%
Other values (26) 1454
37.7%

GitHub Description
Text

MISSING 

Distinct1057
Distinct (%)98.9%
Missing54
Missing (%)4.8%
Memory size8.9 KiB
2023-10-16T11:47:50.028068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10997
Median length343
Mean length134.06642
Min length7

Characters and Unicode

Total characters143317
Distinct characters111
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1045 ?
Unique (%)97.8%

Sample

1st rowFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
2nd rowThe fundamental package for scientific computing with Python.
3rd rowApache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
4th rowDuckDB is an in-process SQL OLAP Database Management System
5th rowApache Parquet
ValueCountFrequency (%)
analysis 950
 
4.7%
of 791
 
3.9%
and 673
 
3.3%
the 669
 
3.3%
for 654
 
3.3%
cell 517
 
2.6%
single-cell 468
 
2.3%
data 451
 
2.2%
single 396
 
2.0%
a 366
 
1.8%
Other values (4025) 14177
70.5%
2023-10-16T11:47:50.328830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19189
13.4%
e 12687
 
8.9%
a 9933
 
6.9%
i 9819
 
6.9%
n 8845
 
6.2%
s 8728
 
6.1%
l 8228
 
5.7%
o 7918
 
5.5%
t 7893
 
5.5%
r 6159
 
4.3%
Other values (101) 43918
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111625
77.9%
Space Separator 19228
 
13.4%
Uppercase Letter 7159
 
5.0%
Other Punctuation 2195
 
1.5%
Dash Punctuation 1305
 
0.9%
Decimal Number 1194
 
0.8%
Close Punctuation 235
 
0.2%
Open Punctuation 229
 
0.2%
Math Symbol 51
 
< 0.1%
Final Punctuation 40
 
< 0.1%
Other values (6) 56
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12687
11.4%
a 9933
 
8.9%
i 9819
 
8.8%
n 8845
 
7.9%
s 8728
 
7.8%
l 8228
 
7.4%
o 7918
 
7.1%
t 7893
 
7.1%
r 6159
 
5.5%
c 4959
 
4.4%
Other values (19) 26456
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 1116
15.6%
S 777
10.9%
R 737
10.3%
C 708
9.9%
N 606
8.5%
T 405
 
5.7%
D 359
 
5.0%
P 329
 
4.6%
I 320
 
4.5%
M 289
 
4.0%
Other values (16) 1513
21.1%
Other Punctuation
ValueCountFrequency (%)
. 909
41.4%
, 624
28.4%
: 186
 
8.5%
/ 173
 
7.9%
" 169
 
7.7%
% 40
 
1.8%
' 37
 
1.7%
; 18
 
0.8%
& 14
 
0.6%
! 7
 
0.3%
Other values (4) 18
 
0.8%
Other Symbol
ValueCountFrequency (%)
6
37.5%
🐟 1
 
6.2%
🏔 1
 
6.2%
🌍 1
 
6.2%
🍱 1
 
6.2%
🍣 1
 
6.2%
🦀 1
 
6.2%
🔬 1
 
6.2%
🧬 1
 
6.2%
🌸 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 262
21.9%
0 252
21.1%
1 207
17.3%
3 92
 
7.7%
9 86
 
7.2%
5 70
 
5.9%
7 70
 
5.9%
4 62
 
5.2%
8 48
 
4.0%
6 45
 
3.8%
Math Symbol
ValueCountFrequency (%)
= 22
43.1%
+ 15
29.4%
> 9
17.6%
< 4
 
7.8%
~ 1
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 1302
99.8%
2
 
0.2%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
19189
99.8%
  39
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 232
98.7%
] 3
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 226
98.7%
[ 3
 
1.3%
Final Punctuation
ValueCountFrequency (%)
32
80.0%
8
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Initial Punctuation
ValueCountFrequency (%)
8
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118783
82.9%
Common 24531
 
17.1%
Inherited 1
 
< 0.1%
Han 1
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
19189
78.2%
- 1302
 
5.3%
. 909
 
3.7%
, 624
 
2.5%
2 262
 
1.1%
0 252
 
1.0%
) 232
 
0.9%
( 226
 
0.9%
1 207
 
0.8%
: 186
 
0.8%
Other values (44) 1142
 
4.7%
Latin
ValueCountFrequency (%)
e 12687
 
10.7%
a 9933
 
8.4%
i 9819
 
8.3%
n 8845
 
7.4%
s 8728
 
7.3%
l 8228
 
6.9%
o 7918
 
6.7%
t 7893
 
6.6%
r 6159
 
5.2%
c 4959
 
4.2%
Other values (44) 33614
28.3%
Inherited
ValueCountFrequency (%)
1
100.0%
Han
ValueCountFrequency (%)
1
100.0%
Greek
ValueCountFrequency (%)
α 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143204
99.9%
None 54
 
< 0.1%
Punctuation 51
 
< 0.1%
Geometric Shapes 6
 
< 0.1%
VS 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19189
13.4%
e 12687
 
8.9%
a 9933
 
6.9%
i 9819
 
6.9%
n 8845
 
6.2%
s 8728
 
6.1%
l 8228
 
5.7%
o 7918
 
5.5%
t 7893
 
5.5%
r 6159
 
4.3%
Other values (79) 43805
30.6%
None
ValueCountFrequency (%)
  39
72.2%
ü 3
 
5.6%
🐟 1
 
1.9%
🏔 1
 
1.9%
🌍 1
 
1.9%
🍱 1
 
1.9%
🍣 1
 
1.9%
🦀 1
 
1.9%
é 1
 
1.9%
🔬 1
 
1.9%
Other values (4) 4
 
7.4%
Punctuation
ValueCountFrequency (%)
32
62.7%
8
 
15.7%
8
 
15.7%
2
 
3.9%
1
 
2.0%
Geometric Shapes
ValueCountFrequency (%)
6
100.0%
VS
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

GitHub Topics
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Organization
Text

MISSING 

Distinct189
Distinct (%)63.6%
Missing826
Missing (%)73.6%
Memory size8.9 KiB
2023-10-16T11:47:50.523210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length106
Median length56
Mean length20.3367
Min length3

Characters and Unicode

Total characters6040
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)48.8%

Sample

1st rowpandas
2nd rowNumPy
3rd rowThe Apache Software Foundation
4th rowDuckDB
5th rowThe Apache Software Foundation
ValueCountFrequency (%)
lab 120
 
13.9%
bioinformatics 24
 
2.8%
group 19
 
2.2%
biology 18
 
2.1%
institute 16
 
1.9%
core 16
 
1.9%
the 15
 
1.7%
computational 15
 
1.7%
of 14
 
1.6%
14
 
1.6%
Other values (316) 592
68.6%
2023-10-16T11:47:50.803964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
572
 
9.5%
a 549
 
9.1%
e 432
 
7.2%
i 401
 
6.6%
o 383
 
6.3%
n 343
 
5.7%
r 327
 
5.4%
t 295
 
4.9%
s 228
 
3.8%
l 189
 
3.1%
Other values (61) 2321
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4367
72.3%
Uppercase Letter 1009
 
16.7%
Space Separator 572
 
9.5%
Other Punctuation 37
 
0.6%
Dash Punctuation 28
 
0.5%
Open Punctuation 9
 
0.1%
Close Punctuation 9
 
0.1%
Decimal Number 7
 
0.1%
Final Punctuation 1
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 549
12.6%
e 432
9.9%
i 401
 
9.2%
o 383
 
8.8%
n 343
 
7.9%
r 327
 
7.5%
t 295
 
6.8%
s 228
 
5.2%
l 189
 
4.3%
b 181
 
4.1%
Other values (16) 1039
23.8%
Uppercase Letter
ValueCountFrequency (%)
L 147
14.6%
C 126
12.5%
B 101
10.0%
S 84
 
8.3%
I 72
 
7.1%
T 64
 
6.3%
G 47
 
4.7%
R 44
 
4.4%
D 41
 
4.1%
P 40
 
4.0%
Other values (16) 243
24.1%
Other Punctuation
ValueCountFrequency (%)
@ 11
29.7%
' 9
24.3%
. 7
18.9%
/ 3
 
8.1%
, 3
 
8.1%
& 2
 
5.4%
: 2
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
1 2
28.6%
2 1
14.3%
9 1
14.3%
3 1
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 25
89.3%
3
 
10.7%
Space Separator
ValueCountFrequency (%)
572
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5376
89.0%
Common 664
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 549
 
10.2%
e 432
 
8.0%
i 401
 
7.5%
o 383
 
7.1%
n 343
 
6.4%
r 327
 
6.1%
t 295
 
5.5%
s 228
 
4.2%
l 189
 
3.5%
b 181
 
3.4%
Other values (42) 2048
38.1%
Common
ValueCountFrequency (%)
572
86.1%
- 25
 
3.8%
@ 11
 
1.7%
' 9
 
1.4%
( 9
 
1.4%
) 9
 
1.4%
. 7
 
1.1%
/ 3
 
0.5%
, 3
 
0.5%
3
 
0.5%
Other values (9) 13
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6035
99.9%
Punctuation 4
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
572
 
9.5%
a 549
 
9.1%
e 432
 
7.2%
i 401
 
6.6%
o 383
 
6.3%
n 343
 
5.7%
r 327
 
5.4%
t 295
 
4.9%
s 228
 
3.8%
l 189
 
3.1%
Other values (58) 2316
38.4%
Punctuation
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
None
ValueCountFrequency (%)
á 1
100.0%

GitHub Network Count
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct97
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.023152
Minimum0
Maximum16810
Zeros435
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:50.897296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile54.8
Maximum16810
Range16810
Interquartile range (IQR)6

Descriptive statistics

Standard deviation574.99266
Coefficient of variation (CV)14.734654
Kurtosis691.24099
Mean39.023152
Median Absolute Deviation (MAD)1
Skewness25.304919
Sum43823
Variance330616.56
MonotonicityNot monotonic
2023-10-16T11:47:50.971430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
38.7%
1 166
 
14.8%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
6 29
 
2.6%
5 25
 
2.2%
8 23
 
2.0%
9 20
 
1.8%
7 18
 
1.6%
Other values (87) 195
17.4%
ValueCountFrequency (%)
0 435
38.7%
1 166
 
14.8%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
5 25
 
2.2%
6 29
 
2.6%
7 18
 
1.6%
8 23
 
2.0%
9 20
 
1.8%
ValueCountFrequency (%)
16810 1
0.1%
8646 1
0.1%
3096 1
0.1%
1332 1
0.1%
1157 1
0.1%
851 1
0.1%
536 1
0.1%
474 1
0.1%
473 1
0.1%
422 1
0.1%

GitHub Detected Languages
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Date Created
Date

UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2010-08-24 01:37:33+00:00
Maximum2023-10-10 14:24:36+00:00
2023-10-16T11:47:51.047649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:51.122073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2011-06-21 13:44:00+00:00
Maximum2023-10-16 15:44:24+00:00
2023-10-16T11:47:51.192333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:51.271689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Duration Created to Most Recent Commit
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Created to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Repository Size (KB)
Real number (ℝ)

Distinct1010
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75420.917
Minimum1
Maximum1922901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:51.356624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.1
Q1688
median10748
Q365047
95-th percentile360260.2
Maximum1922901
Range1922900
Interquartile range (IQR)64359

Descriptive statistics

Standard deviation186112.03
Coefficient of variation (CV)2.4676447
Kurtosis39.984742
Mean75420.917
Median Absolute Deviation (MAD)10707
Skewness5.5454072
Sum84697690
Variance3.4637687 × 1010
MonotonicityNot monotonic
2023-10-16T11:47:51.441525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 10
 
0.9%
26 7
 
0.6%
13 6
 
0.5%
3 6
 
0.5%
67 5
 
0.4%
16 5
 
0.4%
24 5
 
0.4%
22 4
 
0.4%
30 4
 
0.4%
9 4
 
0.4%
Other values (1000) 1067
95.0%
ValueCountFrequency (%)
1 2
 
0.2%
2 2
 
0.2%
3 6
0.5%
4 3
 
0.3%
5 3
 
0.3%
6 10
0.9%
7 3
 
0.3%
8 3
 
0.3%
9 4
 
0.4%
10 1
 
0.1%
ValueCountFrequency (%)
1922901 1
0.1%
1921765 1
0.1%
1792729 1
0.1%
1590023 1
0.1%
1496132 1
0.1%
1461986 1
0.1%
1447466 1
0.1%
1293566 1
0.1%
1245051 1
0.1%
1068214 1
0.1%

GitHub Repo Archived
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1110 
True
 
13
ValueCountFrequency (%)
False 1110
98.8%
True 13
 
1.2%
2023-10-16T11:47:51.505198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Duration Created to Now in Years
Real number (ℝ)

HIGH CORRELATION 

Distinct917
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.846063
Minimum0.016438356
Maximum13.153425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T11:47:51.564248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.016438356
5-th percentile0.64136986
Q12.2383562
median3.5424658
Q35.2767123
95-th percentile7.8227397
Maximum13.153425
Range13.136986
Interquartile range (IQR)3.0383562

Descriptive statistics

Standard deviation2.2608857
Coefficient of variation (CV)0.58784417
Kurtosis0.82613703
Mean3.846063
Median Absolute Deviation (MAD)1.4767123
Skewness0.76387459
Sum4319.1288
Variance5.1116042
MonotonicityNot monotonic
2023-10-16T11:47:51.733951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.695890411 4
 
0.4%
4.142465753 4
 
0.4%
2.410958904 3
 
0.3%
2.252054795 3
 
0.3%
0.7369863014 3
 
0.3%
3.923287671 3
 
0.3%
5.520547945 3
 
0.3%
4.383561644 3
 
0.3%
3.682191781 3
 
0.3%
3.504109589 3
 
0.3%
Other values (907) 1091
97.2%
ValueCountFrequency (%)
0.01643835616 1
0.1%
0.09315068493 1
0.1%
0.09589041096 1
0.1%
0.1452054795 1
0.1%
0.1506849315 1
0.1%
0.1643835616 1
0.1%
0.1726027397 2
0.2%
0.1753424658 2
0.2%
0.2219178082 2
0.2%
0.2328767123 1
0.1%
ValueCountFrequency (%)
13.15342466 1
0.1%
13.09589041 1
0.1%
13.07945205 1
0.1%
12.53972603 1
0.1%
12.21643836 1
0.1%
12.1369863 1
0.1%
12.07671233 1
0.1%
11.28767123 1
0.1%
11.09863014 1
0.1%
10.60821918 1
0.1%

Negative Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

category
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
related-tools-github-query-result
1004 
cytomining-ecosystem-adjacent-tools
 
100
cytomining-ecosystem-relevant-open-source
 
8
microscopy-analysis-tools
 
7
loi-focus
 
4

Length

Max length41
Median length33
Mean length33.099733
Min length9

Characters and Unicode

Total characters37171
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcytomining-ecosystem-relevant-open-source
2nd rowcytomining-ecosystem-relevant-open-source
3rd rowcytomining-ecosystem-relevant-open-source
4th rowcytomining-ecosystem-relevant-open-source
5th rowcytomining-ecosystem-relevant-open-source

Common Values

ValueCountFrequency (%)
related-tools-github-query-result 1004
89.4%
cytomining-ecosystem-adjacent-tools 100
 
8.9%
cytomining-ecosystem-relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Length

2023-10-16T11:47:51.803994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T11:47:51.859303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
related-tools-github-query-result 1004
89.4%
cytomining-ecosystem-adjacent-tools 100
 
8.9%
cytomining-ecosystem-relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 4447
12.0%
- 4366
11.7%
e 4364
11.7%
l 3138
8.4%
r 3035
8.2%
u 3024
8.1%
o 2476
 
6.7%
s 2364
 
6.4%
i 1238
 
3.3%
y 1234
 
3.3%
Other values (13) 7485
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32805
88.3%
Dash Punctuation 4366
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4447
13.6%
e 4364
13.3%
l 3138
9.6%
r 3035
9.3%
u 3024
9.2%
o 2476
 
7.5%
s 2364
 
7.2%
i 1238
 
3.8%
y 1234
 
3.8%
a 1226
 
3.7%
Other values (12) 6259
19.1%
Dash Punctuation
ValueCountFrequency (%)
- 4366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32805
88.3%
Common 4366
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4447
13.6%
e 4364
13.3%
l 3138
9.6%
r 3035
9.3%
u 3024
9.2%
o 2476
 
7.5%
s 2364
 
7.2%
i 1238
 
3.8%
y 1234
 
3.8%
a 1226
 
3.7%
Other values (12) 6259
19.1%
Common
ValueCountFrequency (%)
- 4366
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4447
12.0%
- 4366
11.7%
e 4364
11.7%
l 3138
8.4%
r 3035
8.2%
u 3024
8.1%
o 2476
 
6.7%
s 2364
 
6.4%
i 1238
 
3.3%
y 1234
 
3.3%
Other values (13) 7485
20.1%

Interactions

2023-10-16T11:47:46.270214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:41.866383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.514334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.113099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.627222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.122227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.654193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.140075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.654595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.326647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:41.996038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.574434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.171735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.682309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.182533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.709464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.198911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.714945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.384461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.107651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.634337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.230073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.741229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.241308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.766854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.256830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.775482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.441124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.169502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.692104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.287233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.795687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.301480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.821537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.314633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.917288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.493583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.224575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.748286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.343050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.847866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.356917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.871401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.368441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.972871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.552656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.283979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.808500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.402464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.905001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.418042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.926103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.427991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.036716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.605475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.337129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.861165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.454616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.955535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.472053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.974369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.479932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.091142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.661456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.396874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.919351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.511915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.009688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.533297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.028773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.537485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.150730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.724146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:42.459554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.055031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:43.570941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.069385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:44.595419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.087938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:45.597957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T11:47:46.211600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-16T11:47:51.910629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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Missing values

2023-10-16T11:47:46.823336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-16T11:47:47.000167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-16T11:47:47.110716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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0pandas858127https://pandas.pydata.orghttps://github.com/pandas-dev/pandas[cytomining-ecosystem-relevant-open-source]400281681011213661411BSD-3-ClauseFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more[alignment, data-analysis, data-science, flexible, pandas, python]pandas16810{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 386224.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 6804.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1277513.0, 'D': None, 'Dockerfile': 5751.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 457000.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': 10664.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 20323714.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 14392.0, 'Singularity': None, 'Smarty': 8486.0, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': 1196.0, 'eC': None, 'sed': None}2010-08-24 01:37:33+00:002023-10-16 13:28:00+00:004801 days 11:50:274801 days 16:10:06.3765660 days 02:21:00.548031334787False13.153425-1 days +21:38:59.451969cytomining-ecosystem-relevant-open-source
1numpy908607https://numpy.orghttps://github.com/numpy/numpy[cytomining-ecosystem-relevant-open-source]2474086465952196435BSD-3-ClauseThe fundamental package for scientific computing with Python.[numpy, python]NumPy8646{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 6220071.0, 'C#': None, 'C++': 205725.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 151476.0, 'D': 19.0, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': 3787.0, 'Fortran': 27683.0, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 1697.0, 'Mako': None, 'Mercury': None, 'Meson': 88875.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 10458450.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 17058.0, 'Singularity': None, 'Smarty': 4129.0, 'Stan': None, 'Standard ML': None, 'Starlark': 1842.0, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 5699.0}2010-09-13 23:02:39+00:002023-10-16 14:25:39+00:004780 days 15:23:004780 days 18:45:00.3765660 days 01:23:21.548031131902False13.095890-1 days +22:36:38.451969cytomining-ecosystem-relevant-open-source
2arrow51905353https://arrow.apache.org/https://github.com/apache/arrow[cytomining-ecosystem-relevant-open-source]1261430963513910367Apache-2.0Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing[arrow]The Apache Software Foundation3096{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 3709.0, 'Batchfile': 32824.0, 'C': 1507496.0, 'C#': 1505684.0, 'C++': 26864776.0, 'CMake': 732440.0, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1708430.0, 'D': None, 'Dockerfile': 135006.0, 'Emacs Lisp': 1064.0, 'Forth': None, 'Fortran': None, 'FreeMarker': 2312.0, 'Gnuplot': None, 'Go': 5619807.0, 'Groovy': None, 'HCL': None, 'HTML': 5604.0, 'Hack': None, 'ImageJ Macro': None, 'Java': 7353737.0, 'JavaScript': 128685.0, 'Jinja': 21888.0, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': 8771.0, 'M': None, 'M4': None, 'MATLAB': 722935.0, 'Makefile': 32659.0, 'Mako': None, 'Mercury': None, 'Meson': 62865.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': 11472.0, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 3288210.0, 'QMake': None, 'R': 1698163.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': 1794908.0, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 411936.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 461913.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 34537.0, 'TypeScript': 1108325.0, 'VBScript': None, 'Vala': 24798.0, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 1256.0}2016-02-17 08:00:23+00:002023-10-16 15:32:21+00:002798 days 07:31:582798 days 09:47:16.3765660 days 00:16:39.548031170924False7.665753-1 days +23:43:20.451969cytomining-ecosystem-relevant-open-source
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4parquet-mr20675636https://github.com/apache/parquet-mr[cytomining-ecosystem-relevant-open-source]2179133295130190Apache-2.0Apache Parquet[big-data, java, parquet]The Apache Software Foundation1332{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 5923431.0, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 14771.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': 8436.0, 'Scheme': None, 'Scilab': None, 'Shell': 14860.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 10354.0, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2014-06-10 07:00:07+00:002023-10-16 15:19:17+00:003415 days 08:19:103415 days 10:47:32.3765660 days 00:29:43.54803118475False9.356164-1 days +23:30:16.451969cytomining-ecosystem-relevant-open-source
5snakemake212840200https://snakemake.readthedocs.iohttps://github.com/snakemake/snakemake[cytomining-ecosystem-relevant-open-source]195547422906280MITThis is the development home of the workflow management system Snakemake. For general information, see[reproducibility, snakemake, workflow-management]Snakemake474{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1346.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 3033.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 1727.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 3647966.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': 43973.0, 'Jinja': 6950.0, 'Julia': 334.0, 'Jupyter Notebook': 4389.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 4400.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': 5.0, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1336865.0, 'QMake': None, 'R': 786.0, 'Raku': None, 'Reason': None, 'Rebol': 6.0, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': 6.0, 'Ruby': None, 'Rust': 3617.0, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 5149.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': 5722.0, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2019-10-04 14:58:11+00:002023-10-16 09:50:35+00:001472 days 18:52:241473 days 02:49:28.3765660 days 05:58:25.54803192014False4.035616-1 days +18:01:34.451969cytomining-ecosystem-relevant-open-source
6seurat35927665http://www.satijalab.org/seurathttps://github.com/satijalab/seurat[cytomining-ecosystem-adjacent-tools]19478517822983NOASSERTIONR toolkit for single cell genomics[cran, human-cell-atlas, single-cell-genomics, single-cell-rna-seq]None851{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 166.0, 'C#': None, 'C++': 103887.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 1272988.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 942.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2015-05-20 05:23:02+00:002023-10-03 21:28:12+00:003058 days 16:05:103071 days 12:24:37.37656612 days 18:20:48.54803122722False8.413699-13 days +05:39:11.451969cytomining-ecosystem-adjacent-tools
7napari144513571https://napari.orghttps://github.com/napari/napari[microscopy-analysis-tools]1912389491019150BSD-3-Clausenapari: a fast, interactive, multi-dimensional image viewer for python[napari, numpy, python, visualization]napari389{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 465.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 6722.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 4637636.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': 2846.0, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 1221.0, 'Singularity': 95.0, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-08-13 01:12:28+00:002023-10-16 15:20:03+00:001890 days 14:07:351890 days 16:35:11.3765660 days 00:28:57.54803176651False5.178082-1 days +23:31:02.451969microscopy-analysis-tools
8scanpy80342493https://scanpy.readthedocs.iohttps://github.com/scverse/scanpy[cytomining-ecosystem-adjacent-tools]161153651547125BSD-3-ClauseSingle-cell analysis in Python. Scales to >1M cells.[anndata, bioinformatics, data-science, machine-learning, python, scanpy, scverse, transcriptomics, visualize-data]scverse536{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1292128.0, 'QMake': None, 'R': 2315.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2017-01-29 11:31:11+00:002023-10-16 15:40:34+00:002451 days 04:09:232451 days 06:16:28.3765660 days 00:08:26.54803139269False6.715068-1 days +23:51:33.451969cytomining-ecosystem-adjacent-tools
9STAR17778869https://github.com/alexdobin/STAR[cytomining-ecosystem-adjacent-tools]15924738670134MITRNA-seq aligner[]None473{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 24345.0, 'Batchfile': None, 'C': 2350683.0, 'C#': None, 'C++': 1210764.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 676.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 1006.0, 'Makefile': 25383.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': 11364.0, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 295.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': 119547.0, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2014-03-15 15:47:05+00:002023-09-08 12:23:35+00:003463 days 20:36:303502 days 02:00:34.37656638 days 03:25:25.548031541229False9.594521-39 days +20:34:34.451969cytomining-ecosystem-adjacent-tools
Project NameGitHub Repository IDProject HomepageProject Repo URLProject Landscape CategoryGitHub StarsGitHub ForksGitHub SubscribersGitHub Open IssuesGitHub ContributorsGitHub License TypeGitHub DescriptionGitHub TopicsGitHub OrganizationGitHub Network CountGitHub Detected LanguagesDate CreatedDate Most Recent CommitDuration Created to Most Recent CommitDuration Created to NowDuration Most Recent Commit to NowRepository Size (KB)GitHub Repo ArchivedDuration Created to Now in YearsNegative Duration Most Recent Commit to Nowcategory
1113BloodVesselImageAnalysis323621577https://github.com/RoopaMadhu/BloodVesselImageAnalysis[related-tools-github-query-result]00101NoneSpatiographic projections of biological systems[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 188297.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-12-22 12:36:17+00:002020-12-22 14:22:59+00:000 days 01:46:421028 days 05:11:22.3765661028 days 01:26:01.548031576False2.816438-1029 days +22:33:58.451969related-tools-github-query-result
1114SickleCellDataAnalysis298584969Nonehttps://github.com/ridz46/SickleCellDataAnalysis[related-tools-github-query-result]00101NoneThis repository contains the codes used for image processing and further downstream data analysis for the project on the development of a point-of-care diagnostic test for sickle cell disease. This repository is maintained by the Microfluidics & Biological Physics group, Department of Biosciences & Bioengineering, Indian Institute of Technology Bombay, Mumbai, India.[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 4373.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-09-25 13:45:27+00:002020-09-28 08:55:24+00:002 days 19:09:571116 days 04:02:12.3765661113 days 06:53:36.54803117False3.057534-1114 days +17:06:23.451969related-tools-github-query-result
1115Data-Analysis-AMATH-482245942374Nonehttps://github.com/priyanshir/Data-Analysis-AMATH-482[related-tools-github-query-result]00101NoneExploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 703573.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 33375.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-03-09 04:17:02+00:002020-03-14 22:41:50+00:005 days 18:24:481316 days 13:30:37.3765661310 days 17:07:10.5480312832False3.605479-1311 days +06:52:49.451969related-tools-github-query-result
1116Aging-Cell-Morphology-Cell-transformations-and-image-processing235024426Nonehttps://github.com/sumisingh/Aging-Cell-Morphology-Cell-transformations-and-image-processing[related-tools-github-query-result]00101NoneIdentifying cellular transformations associated with aging using image processing[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 8130261.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-01-20 05:18:57+00:002020-01-20 05:29:56+00:000 days 00:10:591365 days 12:28:42.3765661365 days 10:19:04.54803127874False3.739726-1366 days +13:40:55.451969related-tools-github-query-result
1117CellMorphology148259959https://github.com/KnightofDawn/CellMorphology[related-tools-github-query-result]00101NonePython code to identify/analyze cells from microscopic stack images.[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 65906.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-09-11 04:25:09+00:002018-09-10 20:41:28+00:00-1 days +16:16:191861 days 13:22:30.3765661861 days 19:07:32.548031161False5.098630-1862 days +04:52:27.451969related-tools-github-query-result
1118ImagingCells137526283Nonehttps://github.com/jesnyder/ImagingCells[related-tools-github-query-result]00101NoneScripts to analyze cell number and morphologies using images.[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 1523.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-06-15 19:50:00+00:002018-08-31 19:21:33+00:0076 days 23:31:331948 days 21:57:39.3765661871 days 20:27:27.5480311False5.336986-1872 days +03:32:32.451969related-tools-github-query-result
1119course-bia119301640Nonehttps://github.com/denzf/course-bia[related-tools-github-query-result]00101MITCode examples for the course of Biological Image Analysis[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 6010.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-01-28 21:58:13+00:002018-01-24 03:22:19+00:00-5 days +05:24:062086 days 19:49:26.3765662091 days 12:26:41.548031203False5.715068-2092 days +11:33:18.451969related-tools-github-query-result
1120Cell-virulence-Detection-using-Image-Processing163268436Nonehttps://github.com/arushigupta148/Cell-virulence-Detection-using-Image-Processing[related-tools-github-query-result]00001NoneDesigned an automated tool to find the thickness of multiple cell capsules from images using morphological operations to generate plots of cell size vs capsular thickness, simplifying detection of virulence in yeast cells for mycologists[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 12989.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-12-27 08:27:06+00:002019-05-12 23:09:02+00:00136 days 14:41:561754 days 09:20:33.3765661617 days 16:39:58.5480311751False4.805479-1618 days +07:20:01.451969related-tools-github-query-result
1121Image-analysis152904377https://github.com/dguin/Image-analysis[related-tools-github-query-result]00001NoneThe repository contains code to analyze videos where each frame is a snapshot of the cellular status as a function of time. The program includes subroutines for segmentation protocols to pick a cell and differentiate it from the background when the signal to noise is low. The protocol docx explains what each code does and explains the order in which they must be run. As is the program analyzes FRET data from a cell, where the temperature increases as a function of time and one can evaluate the changes in the cell morphology as the cell is under heat stress[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 40670.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-10-13 18:53:42+00:002018-10-13 19:32:22+00:000 days 00:38:401828 days 22:53:57.3765661828 days 20:16:38.54803126False5.008219-1829 days +03:43:21.451969related-tools-github-query-result
1122oct-image-analysis125785309https://github.com/ricster101/oct-image-analysis[related-tools-github-query-result]00001NoneWork developed with Adriana Costa during the course of Computer Vision and Biological Perception aiming to discover differences in mice retina[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 22755.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-03-19 01:29:32+00:002018-03-19 03:31:25+00:000 days 02:01:532037 days 16:18:07.3765662037 days 12:17:35.54803116False5.580822-2038 days +11:42:24.451969related-tools-github-query-result